AIMC Topic: Diagnosis, Computer-Assisted

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The implementation of computer-aided detection in an initial endoscopy training improves the quality measures of trainees' future colonoscopies: a retrospective cohort study.

Surgical endoscopy
INTRODUCTION: The implementation of computer-aided detection (CADe) systems has resulted in a growing number of young endoscopists being trained using AI-enhanced devices. The potential impact of AI-enhanced training on the trainees' future performan...

Machine learning strategies for multi-label pre-diagnosis of diseases with superficial data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: General practice (GP) pre-diagnosis, a key task in disease triage, directs patients to suitable departments despite limited data and multi-label classification challenges. To address this issue, a framework with dimensionali...

Image-Based Diagnostic Performance of LLMs vs CNNs for Oral Lichen Planus: Example-Guided and Differential Diagnosis.

International dental journal
INTRODUCTION AND AIMS: The overlapping characteristics of oral lichen planus (OLP), a chronic oral mucosal inflammatory condition, with those of other oral lesions, present diagnostic challenges. Large language models (LLMs) with integrated computer-...

Advances in disease detection through retinal imaging: A systematic review.

Computers in biology and medicine
Ocular and non-ocular diseases significantly impact millions of people worldwide, leading to vision impairment or blindness if not detected and managed early. Many individuals could be prevented from becoming blind by treating these diseases early on...

A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.

Scientific reports
In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource di...

Impact of AI-Generated ADC Maps on Computer-Aided Diagnosis of Prostate Cancer: A Feasibility Study.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the impact of AI-generated apparent diffusion coefficient (ADC) maps on diagnostic performance of a 3D U-Net AI model for prostate cancer (PCa) detection and segmentation at biparametric MRI (bpMRI).

Improving cancer detection through computer-aided diagnosis: A comprehensive analysis of nonlinear and texture features in breast thermograms.

PloS one
Breast cancer is a significant health issue for women, characterized by its high rates of mortality and sickness. However, its early detection is crucial for improving patient outcomes. Thermography, which measures temperature variations between heal...

Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.

Scientific reports
Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utiliz...

Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.

Scientific reports
White blood cell (WBC) classification is a crucial step in assessing a patient's health and validating medical treatment in the medical domain. Hence, efficient computer vision solutions to the classification of WBC will be an effective aid to medica...

Computer-aided diagnosis tool utilizing a deep learning model for preoperative T-staging of rectal cancer based on three-dimensional endorectal ultrasound.

Abdominal radiology (New York)
BACKGROUND: The prognosis and treatment outcomes for patients with rectal cancer are critically dependent on an accurate and comprehensive preoperative evaluation.Three-dimensional endorectal ultrasound (3D-ERUS) has demonstrated high accuracy in the...